方法对比

	稳健潜类别分析	稳健探索性因子分析
领域	统计学	心理测量学
方法族	Latent structure	Latent structure
起源年份	2000s	2000–2003
提出者	Building on Hennig (2004) and Vermunt & Magidson (2004)	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)
类型	Robust latent variable / mixture model	Latent variable / dimension reduction (robust)
开创性文献	Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340.	Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.
别名	robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation
相关	6	4
摘要	Robust latent class analysis (robust LCA) extends the standard latent class model by incorporating outlier-resistant estimation techniques — such as trimmed likelihood, M-estimation, or downweighting — so that atypical response patterns do not distort the recovered class structure or class membership probabilities.	Robust exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard EFA but replaces classical covariance estimation with robust counterparts — such as minimum covariance determinant or iteratively reweighted least squares — so that a small fraction of atypical cases cannot distort the recovered factor loadings.